Uncovering insights on the resource tracking of three semi-arid ungulate species based on remotely recorded high-resolution and long-term data
Robert Hering, Morgan Hauptfleisch, Stephanie Kramer-Schadt, Taylor Smith, Niels Blaum
09 Juni, 2023
Abstract
The recent advances in remote data acquisition of the earths surface and the movement of animals fundamentally improved our understanding of the relationships between changing environmental conditions and the positioning of animals. In ungulates the positioning in space largely varies along with the temporal availability of resources, resulting in diverse movement tactics. Remarkably, movement tactics not just vary among species but also within populations and even within individuals. However, the principles leading to this variation remain rather undiscovered, although their understanding will be crucial to sustain wildlife under the future challenges of climate change and increased land-use. Combining high-resolution and long-term data on vegetation and the movement of three ungulate species, we exemplary demonstrate how remote sensing helps to identify and understand resource tracking and, thus, can scientifically guide policy development for the future of wild animals.
Novelty
We show that identifying and quantifying so called resource tracking in ungulates will largely benefit from precise remote sensing and animal tracking data. For this we use Landsat and Sentinel NDVI time series and field observations to map 13 vegetation types at a 30 m x 30 m resolution in a semi-arid savanna landscape (Fig.: 1).
Figure 1: Results of vegetation classification based on remote sensing (NDVI from Sentinel-2 and Landsat-8 missions) for the study area. Classified vegetation types and abbreviations are shown in the legend.
We then use over 2 years of animal tracking data from 24 individuals of three antelope species (springbok, kudu, eland) at a high temporal resolution (GPS positions every 5 - 15 minutes) to identify movement phases (Fig.: 2) and foraging patches (Fig.: 3) .
Figure 2: Example movement data of one group of eland for two years (panels). Single movement phases shown in different colors indicating the main vegetation used. Vegetative composition of foraging patches per movement phase shown in pie charts. Same colors used as in Fig. 1.
Figure 3: Example of foraging patch identification. Selected patches (orange pixels) are identified only when the individuals were active but relatively slow.
From the vegetation type specific foraging data, we then derive dynamics in vegetation greenness of the patches (Fig. 4) which allows to identify patterns of resource tracking precisely. For instance, in 2021 a group of eland (the largest antelope species) foraged on a grassland patches (Fig. 4, light green phase in April 2021) right after the peak in greenness when biomass is highest and thus energy intake maximizes. This finding matches theory perfectly.
Figure 4: Example of greenness dynamics on foraging patches. Foreground lines indicate greenness at the time the individual was on the patch and background lines indicate the following dynamics in greenness on that patch. Error bars at bottom display movement phases with color indicating main vegetation used during the respective phase. Same colors used as in Fig.: 1.
With further examples demonstrating theoretical predictions of resource tracking we illustrate the possible insights on ungulate movement ecology when high-resolution satellite imagery is combined with time series analysis and high-resolution animal tracking data and discuss current restrictions when only coarse remote sensing data is used. Further we will discuss the implications of our findings in the light of changing environmental conditions and future conservation policies to protect migratory movements and the associated ecosystem services.
Tracking video
An example video of the tracking data. Note the fence lines have gaps (Hering, Hauptfleisch, Jago, et al. 2022) and that water points (blue dots) pop-up every time they got visited. Graph on bottom shows mean soil adjusted vegetation index (like NDVI but adjusted for arid environments accounting for bare ground) as shaded points and individual daily averages as colored lines. One second in the video corresponds to 3 days of movement data which makes 10 seconds / month.